Research on Edge Detection of LiDAR Images Based on Artificial Intelligence Technology
University of Houston（休斯顿大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；The University of Chicago（芝加哥大学）；Columbia University（哥伦比亚大学）；University of Electronic Science and Technology of China（电子科技大学）
Haowei Yang, Liyang Wang, Jingyu Zhang, Yu Cheng, Ao Xiang
With the widespread application of Light Detection and Ranging (LiDAR) technology in fields such as autonomous driving, robot navigation, and terrain mapping, the importance of edge detection in LiDAR images has become increasingly prominent. Traditional edge detection methods often face challenges in accuracy and computational complexity when processing LiDAR images. To address these issues, this study proposes an edge detection method for LiDAR images based on artificial intelligence technology. This paper first reviews the current state of research on LiDAR technology and image edge detection, introducing common edge detection algorithms and their applications in LiDAR image processing. Subsequently, a deep learning-based edge detection model is designed and implemented, optimizing the model training process through preprocessing and enhancement of the LiDAR image dataset. Experimental results indicate that the proposed method outperforms traditional methods in terms of detection accuracy and computational efficiency, showing significant practical application value. Finally, improvement strategies are proposed for the current method's shortcomings, and the improvements are validated through experiments.
RS2AD-LiDAR: End-to-End Autonomous Driving LiDAR Data Generation from Roadside Sensor Observations
State Key Laboratory of Intelligent Green Vehicle and Mobility（智能绿色车辆与移动国家重点实验室）；School of Vehicle and Mobility（车辆与移动学院）；College of Artificial Intelligence（人工智能学院）；Logic & Silicon AI Studio；School of Instrumentation and Optoelectronic Engineering（仪器科学与光电工程学院）
HPL-ViT: A Unified Perception Framework for Heterogeneous Parallel LiDARs in V2V
Institute of Automation, Chinese Academy of Science（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Waytous Inc.（Waytous公司）；University of Illinois, Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
LiDAR for Crowd Management: Applications, Benefits, and Future Directions
College of Computer Science and Information Systems, Najran University（纳吉兰大学计算机科学与信息系统学院）；College of Innovation & Technology, University of Michigan-Flint（密歇根大学弗林特分校创新与技术学院）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）
Efficient and Privacy-Protecting Background Removal for 2D Video Streaming using iPhone 15 Pro Max LiDAR
Department of Computer Science, Iowa State University, Ames, IA, USA（计算机科学系，爱荷华州立大学）；Department of Computer Science and School of Education, Iowa State University, Ames, IA, USA（计算机科学系与教育学院，爱荷华州立大学）
本研究利用iPhone 15 Pro Max的LiDAR技术，结合GPU图像处理与Metal着色器实现60帧/秒的高效背景去除，该方法不受光照影响，仅受深度数据带宽和LiDAR材质反射局限，未来分辨率提升后有望成为主流背景去除方案。
Talk to Parallel LiDARs: A Human-LiDAR Interaction Method Based on 3D Visual Grounding
Institute of Automation（自动化研究所）；Chinese Academy of Sciences（中国科学院）；College of Electronic Engineering（电子工程学院）；South China Agricultural University（华南农业大学）；School of Computer Science（计算机学院）；Beijing Institute of Technology（北京理工大学）
Lidar-based Object Classification with Explicit Occlusion Modeling
College of Intelligence Science and Technology, National University of Defense Technology（国防科技大学智能科学与技术学院）；Unmanned Systems Research Center, National Innovation Institute of Defense Technology（国防科技创新研究院无人系统研究中心）
Sparse LiDAR and Stereo Fusion (SLS-Fusion) for Depth Estimationand 3D Object Detection
Cerema（法国路桥与道路研究中心）；Institut de Recherche en Informatique de Toulouse（图卢兹计算机科学研究所）；Université de Toulouse（图卢兹大学）；Universidad Carlos III de Madrid（马德里卡洛斯三世大学）；Queen Mary University of London（伦敦玛丽女王大学）
Multimodal Signal Processing For Thermo-Visible-Lidar Fusion In Real-time 3D Semantic Mapping
College of Mechatronics and Control Engineering, Shenzhen University（深圳大学机械与控制工程学院）；School of Robotics, Xi’an-Jiaotong Liverpool University（西安交通大学利物浦大学机器人学院）
